Image search using intersected predicted queries

ABSTRACT

A method for receiving a first user query from a user for searching an item, forming a first filter based on the first user query, and forming a first filtered item collection is provided. The method includes predicting a new query based on the first user query and a historical query log, forming a second filter for the new query, and applying the second filter to the first filtered item collection to form a second filtered item collection. Further, associating an item score to each of a plurality of items in the first and second filtered item collections, sorting the plurality of items in the first and second filtered item collections according to the item score associated to each of the plurality of items, and providing, to a user display, an item in the plurality of items in the first or second filtered item collections according to a sorting order.

BACKGROUND Field

The present disclosure generally relates to an image search engine, andmore particularly to methods and systems to provide image search resultswithin an image database associated with a search engine includingpredicted queries.

Description of the Related Art

In the context of large multimedia databases, the ability of searchengines to rapidly obtain relevant results for a user can be hampered bylarge computational overheads. Users are commonly required to includelong text strings for a search query in order to reduce the number ofitems in a search result. Users become frustrated by inaccurate searchresults that can force users to browse through many pages of results,consequently reducing the advantage of an otherwise rich multimediadatabase.

SUMMARY

According to one embodiment of the present disclosure, a method isprovided for receiving a first user query from a user for searching anitem, forming a first filter based on the first user query, and forminga first filtered item collection including a plurality of items from theitem database. The method also includes predicting a new query based onthe first user query and a historical query log, forming a second filterfor the new query, and applying the second filter to the first filtereditem collection to form a second filtered item collection. The methodfurther includes associating an item score to each of a plurality ofitems in the first filtered item collection and in the second filtereditem collection, sorting the plurality of items in the first filtereditem collection and in the second filtered item collection according tothe item score associated to each of the plurality of items, andproviding, for display to a user in a results panel, at least one itemin the plurality of items in the first filtered item collection and inthe second filtered item collection.

According to one embodiment of the present disclosure, a method isprovided for placing a query for an image search in a user interface fora search engine and selecting a predicted query from a pull down menu inthe user interface. The method further includes placing an additionalterm in the query to focus a search scope, providing a search command tothe search engine, and selecting an image from a thumbnail in a resultspanel when the results panel includes a satisfactory search scope.Selecting a predicted query from a pull down menu in the user interfaceincludes directing a pointing device to the predicted query in the pulldown menu and activating the selection with the pointing device, and themethod also includes re-ordering terms in a new query when the resultspanel does not include a satisfactory search scope.

According to some embodiments, a system is provided that includes one ormore processors and a computer-readable storage medium coupled to theone or more processors, the computer-readable storage medium includinginstructions that, when executed by the one or more processors, causethe one or more processors to receive a first user query from a user forsearching an item in an item database. The instructions causing the oneor more processors further to form a first filter based on the firstuser query, form a first filtered item collection comprising a pluralityof items from the item database, and predict a new query based on thefirst user query and a historical query log. The instructions furthercause the one or more processors to form a second filter for the newquery, apply the second filter to the first filtered item collection toform a second filtered item collection, and associate an item score toeach of a plurality of items in the first filtered item collection andin the second filtered item collection. The computer-readable mediumfurther includes instructions causing the one or more processors to sortthe plurality of items in the first filtered item collection and in thesecond filtered item collection according to the item score associatedto each of the plurality of items, and to provide, for display to a userin a results panel, at least one item from the plurality of items in thefirst filtered item collection and in the second filtered itemcollection.

According to some embodiments, a non-transitory, computer-readablemedium stores instructions that cause a processor to perform a methodincluding the steps of receiving a first user query from a user forsearching an item in an item database, forming a first filter based onthe first user query, and forming a first filtered item collection. Insome embodiments, the instructions cause the processor to perform thesteps of predicting a new query based on the first user query and ahistorical query log, forming a second filter for the new query, andapplying the second filter to the first filtered item collection to forma second filtered item collection comprising a plurality of items fromthe item database. The instructions further cause the processor toperform the steps of associating an item score to each of a plurality ofitems in the first filtered item collection and in the second filtereditem collection, sorting the plurality of items in the first filtereditem collection and in the second filtered item collection according tothe item score associated to each of the plurality of items, andproviding, for display to a user in a results panel, at least one itemin the plurality of items in the first filtered item collection and inthe second filtered item collection.

In yet other embodiments, a system is provided that includes a means forstoring commands and a means for executing the commands, causing thesystem to perform a method including the steps of receiving a first userquery from a user for searching an item in an item database, forming afirst filter based on the first user query, and forming a first filtereditem collection comprising a plurality of items from the item database.In some embodiments, the commands cause the system to perform the stepsof predicting a new query based on the first user query and a historicalquery log, forming a second filter for the new query, and applying thesecond filter to the first filtered item collection to form a secondfiltered item collection. The commands may further cause the system toperform the steps of associating an item score to each of a plurality ofitems in the first filtered item collection and in the second filtereditem collection, sorting the plurality of items in the first filtereditem collection and in the second filtered item collection according tothe item score associated to each of the plurality of items, andproviding, for display to a user in a results panel, at least one itemin the plurality of items in the first filtered item collection and inthe second filtered item collection.

It is understood that other configurations of the subject technologywill become readily apparent to those skilled in the art from thefollowing detailed description, wherein various configurations of thesubject technology are shown and described by way of illustration. Aswill be realized, the subject technology is capable of other anddifferent configurations and its several details are capable ofmodification in various other respects, all without departing from thescope of the subject technology. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide furtherunderstanding and are incorporated in and constitute a part of thisspecification, illustrate disclosed embodiments and together with thedescription serve to explain the principles of the disclosedembodiments. In the drawings:

FIG. 1 illustrates an example architecture for an image search withintersected predicted queries suitable for practicing someimplementations of the disclosure.

FIG. 2 is a block diagram illustrating an example server and client fromthe architecture of FIG. 1 according to certain aspects of thedisclosure.

FIG. 3 is a flow chart illustrating steps in a method for image searchusing intersected predicted queries.

FIG. 4 is a block diagram illustrating a process used to predict a userquery based on a first user query and a historical query log.

FIG. 5 illustrates an exemplary user interface of a search engineconsistent with the present disclosure.

FIG. 6 is a walk-through illustration for searching an image based on apredicted user query from a first user query and a historical query log,according to some embodiments.

FIG. 7 is a flow chart illustrating steps in a method to predict a userquery based on a first user query and a historical query log.

FIG. 8 is a flow chart illustrating steps in a method to select apredicted user query provided to a client device through a userinterface.

FIG. 9 is a block diagram illustrating an example computer system withwhich the client and server of FIG. 2 and the methods of FIGS. 3-8 canbe implemented.

In the figures, elements and steps denoted by the same or similarreference numerals are associated with the same or similar elements andsteps, unless indicated otherwise.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth to provide a full understanding of the present disclosure. It willbe apparent, however, to one ordinarily skilled in the art that theembodiments of the present disclosure may be practiced without some ofthese specific details. In other instances, well-known structures andtechniques have not been shown in detail so as not to obscure thedisclosure.

As used herein, the term “content item” may be used, for example, inreference to a digital file that is composed of one or more mediaelements of different types (text, image, video, audio, etc.). A contentitem can be a single picture or a single video file. The term “imageidentifier” as used herein may refer to a form of metadata such as a tagand/or a label associated with an image for identifying the image.

A search engine as disclosed herein includes an ability to filter acollection of content items to those content items relevant to a searchquery, scoring these filtered results by relevancy, and sorting theresults. In some embodiments, filtering and scoring can be combined as asingle step. For example, the image results for over the query “woman inred dress” are expected to form a subset of the image results for thequery “woman in dress.” Accordingly, a search of the former query overthe latter filter avoids redundancy and is therefore more efficient andbetter targeted. The filtering step can be computationally intensivebecause it involves scanning through the entire image collection.Accordingly, methods as disclosed herein limit the results retrieved tocontent items that are more relevant to the initial user query. Methodsas disclosed herein improve the efficiency of a search engine forfinding search results by simultaneously searching for multiple queries.Embodiments disclosed herein exploit the higher likelihood that a userwill follow a query that is a refinement query of an initial user query,i.e. a refinement query that contains all, or most of the terms in theinitial user query, with additional terms used as refinement.

The disclosed system addresses the problem of filtering results in asearch engine in a large database of content items specifically arisingin the realm of computer technology by providing a solution also rootedin computer technology, namely, by considering the inclusion ofadditional terms in an initial user query for a search engine, expandingthe search scope to a plurality of predicted queries, and performing amore exhaustive and effective search with little to no computationaloverhead by pursuing multiple search threads simultaneously, or almostsimultaneously.

The subject system provides several advantages including providing anextended and more efficient search scope for queries to a search engine.The system provides a machine learning capability where the system canlearn from a content item such as prior search queries from one ormultiple users to expand a search scope, thereby increasing thelikelihood that the user will in fact select at least one or moreresults. Also, the system enables the user to more rapidly andefficiently interact with a search engine, improving user experience andsatisfaction.

Embodiments as disclosed herein add a limited computational overhead byincluding a plurality of inter-related search threads that are executedalmost simultaneously, in addition to performing a more robust search,resulting in providing the user with a more exhaustive and accurate setof search results.

The proposed solution further provides improvements to the functioningof the computer itself because it substantially reduces network usage(e.g., during an image search) while providing search results that aremore likely to be relevant to the user.

Although many examples provided herein describe a user's search inputsbeing identifiable, each user may grant explicit permission for suchuser information to be shared or stored. The explicit permission may begranted using privacy controls integrated into the disclosed system.Each user may be provided notice that such user information will beshared with explicit consent, and each user may at any time end havingthe information shared, and may delete any stored user information. Thestored user information may be encrypted to protect user security.

Example System Architecture

FIG. 1 illustrates an example architecture 100 for an image search withintersected predicted queries suitable for practicing someimplementations of the disclosure. The architecture 100 includes servers130 and clients 110 connected over a network 150. One of the manyservers 130 is configured to host a memory including instructions which,when executed by a processor, cause the server 130 to perform at leastsome of the steps in methods as disclosed herein. In some embodiments,the processor is configured to search and find multimedia data filesincluding images, video, music, and the like using text queries input bya user through client device 110. Further, in some embodiments theprocessor in server 130 is configured to find predicted queries from aninput user query (IUQ), and to search and find multimedia data filesusing the predicted search queries. Accordingly, one of the many servers130 also hosts a collection of images, videos, and multimedia files. Thecollection of multimedia files can be searched using an image searchengine (e.g., accessible through a web page or an application running onone of clients 110). Servers 130 can return images tagged with an imagescore to clients 110 in response to the input user query (IUQ).Moreover, in some embodiments the processor is configured to associatethe IUQ with a predicted query string from a query stream log. Forpurposes of load balancing, multiple servers 130 can host memoriesincluding instructions to one or more processors and multiple servers130 can host the collection of images.

Servers 130 may include any device having an appropriate processor,memory, and communications capability for hosting the collection ofimages and the image search engine. The image search engine isaccessible by various clients 110 over the network 150. Clients 110 canbe, for example, desktop computers, mobile computers, tablet computers(e.g., including e-book readers), mobile devices (e.g., a smartphone orPDA), or any other devices having appropriate processor, memory, andcommunications capabilities for accessing the image search engine on oneof servers 130. Network 150 can include, for example, any one or more ofa local area network (LAN), a wide area network (WAN), the Internet, andthe like. Further, network 150 can include, but is not limited to, anyone or more of the following network topologies, including a busnetwork, a star network, a ring network, a mesh network, a star-busnetwork, tree or hierarchical network, and the like.

Example Automatic Video Summary System

FIG. 2 is a block diagram 200 illustrating an example server 130 andclient 110 in the architecture 100 of FIG. 1 according to certainaspects of the disclosure. Client 110 and server 130 are communicativelycoupled over network 150 via respective communications modules 218 and238. Communications modules 218 and 238 are configured to interface withnetwork 150 to send and receive information, such as data, requests,responses, and commands to other devices on the network. Communicationsmodules 218 and 238 can be, for example, modems or Ethernet cards.

Memory 232 includes an image metadata database 252 and an image searchengine 242 for searching image metadata database 252. In one or moreimplementations, image metadata database 252 represents a database thatcontains, for each image, a mapping from an image identifier to a datafile containing pixel data for the image (e.g., in jpeg format).

Server 130 includes a memory 232, a processor 236, and communicationsmodule 238. Moreover, in some embodiments processor 236 is configured toobtain a set of predicted queries from a query stream log 246, from aninteraction history information in an interaction history database 254,and from an IUQ received from a user through a user interface for imagesearch engine 242. The user interface is displayed for the user in anoutput device 216 of client 110. Query stream log 246 includes aplurality of query strings previously used by one or more usersinteracting with image search engine 242. In some aspects, processor236, using query stream log 246 and executing instructions from memory232, can provide a set of predicted queries from query stream log 246 toimage search engine 242. Processor 236 may also display the set ofpredicted queries to the user in a pull down menu of the user interface.

The user may access image search engine 242 through an application 222or a web browser installed in client 110. Image metadata database 252can be, for example, a dataset associated with images corresponding to anumber of style classes (e.g., about 25). The images may be paired withimage vector information and image cluster information. The image vectorinformation identifies vectors representing a large sample of images(e.g., about 50 million) and the image cluster information identifiesthe vectors in one or more clusters such that each of the cluster ofimages represents a semantic concept.

Although image metadata database 252 and image search engine 242 areillustrated as being in the same memory 232 of a server 130, in certainaspects the image metadata database 252 and image search engine 242 canbe hosted in a memory of a different server but accessible by server 130illustrated in FIG. 2.

Memory 232 also includes interaction history data 254. In certainaspects, processor 236 is configured to determine the interactionhistory data 254 by obtaining user interaction data identifyinginteractions with images from image search results that are responsiveto search queries. For example, the processor 236 may determine that auser interacted with an image from a search result, such as, by clickingon the image, saving the image for subsequent access, or downloaded theimage to a client (e.g., client 110), or the like. The processor 236 maykeep track of the user interactions with a number of images over a giventime period. The interaction history 254 may also include dataindicating search behavior (and/or patterns) relating to prior imagesearch queries.

Processor 236 is configured to execute instructions, such asinstructions physically coded into processor 236, instructions receivedfrom software in memory 232, or a combination of both. The IUQidentifies a user search query in a given natural language. For example,the search query may be entered as an English term or combination ofterms. A user of client 110 may use input device 214 to submit a searchterm or phrase via a user interface of application 222. The userinterface may include an input section where the search term or phrasemay be typed in, for example. The input section may include one or morecontrols to allow the user to initiate the image search upon receivingthe search query. In some aspects, the image search may be initiatedautomatically upon receiving at least one search term (or at least thesearch phrase in part). As described herein, the natural language usedin image search engine 242 is not limited to English, and the naturallanguage can vary to include other natural languages depending onimplementation.

A search query is then provisioned to image search engine 242 forinitiating the image search through image metadata database 252. The IUQis provided, for example, by the user accessing image search engine 242over network 150 using application 222 in memory 220 on client 110. Theuser submits the IUQ using input device 214 of client 110. For example,the user may use input device 214 to enter a text-based search term orphrase. In response to the IUQ, a processor in client 110 transmits thesearch query over the network 150 using communications module 218 ofclient 110 to communications module 238 of server 130.

Processor 236, upon receiving the IUQ, submits a search request to imagesearch engine 242. In some embodiments, processor 236 receives anidentification of a plurality of images from image metadata database 252that are responsive to the IUQ and also to the set of predicted queries.The plurality of images from image metadata database 252 may be sortedaccording to an image score (e.g., using interaction history database254) indicating a probability of a user interaction for each image(e.g., the probability of a user clicking a thumbnail associated withone of the images). Processor 236 may then provide the listing of imagesto application 222 over network 150 for display by output device 216.The listing of images may include a plurality of thumbnails in a resultspanel of the user interface in output device 216.

FIG. 3 is a flow chart illustrating steps in a method 300 for imagesearch using intersected predicted queries. Method 300 may be performedat least partially by any one of network servers hosting a collection ofimages, videos, and multimedia files (e.g., images and video clips),while communicating with any one of a plurality of client devices (e.g.,any one of servers 130 and any one of clients 110). The client devicesmay be handled by a user, wherein the user may be registered to aprivate account with the server, or may be a visitor to the serverwebsite or logged in a server application installed in the clientdevice. At least some of the steps in method 300 may be performed by acomputer having a processor executing commands stored in a memory of thecomputer (e.g., processors 212 and 236, memories 220 and 232). Further,steps as disclosed in method 300 may include retrieving, editing, and/orstoring files in a database that is part of, or is communicably coupledto, the computer, using, inter-alia, an image search engine (e.g., imagesearch engine 242). The database may include any one of an imagedatabase, a query stream log and an interaction history database (e.g.,image database 252, query stream log 246 and interaction historydatabase 254). Methods consistent with the present disclosure mayinclude at least some, but not all of the steps illustrated in method300, performed in a different sequence. Furthermore, methods consistentwith the present disclosure may include at least two or more steps as inmethod 300 performed overlapping in time, or almost simultaneously.

Step 302 includes receiving a first user query. In the presentdisclosure, the input is a user query, which may be referred to as theinitial user query (IUQ). When step 302 is completed, steps 303 and 304may be performed in parallel, simultaneously, or almost simultaneously,as follows.

Step 303 includes forming a filter based on the first user query. Aswith a standard search system, our disclosure then filters the entirecollection by the IUQ. The result of this filtering step may berecognized as the IUQ filter result (IUQFR). Filter all items to trimdown collection (most computationally expensive). In some embodiments,step 303 includes storing the results of the filter in a cache memory(e.g., in memory 232) to be accessed further along in the process.

Step 304 includes predicting a new user query based on the first userquery and a historical log. In some embodiments, step 304 includesdetermining an additional term for a search based on a predicted newquery. In some embodiments, step 304 includes predicting the R mostlikely subsequent queries given the IUQ (where R is a pre-selectedinteger). For each new query determine the difference with the firstuser query in terms of the words added to the new query relative to thefirst user query. In some embodiments, step 304 includes matching atleast a portion of the new query with at least a portion of the firstuser query and a portion of a query from the historical query log.

In some embodiments, step 304 includes re-ordering a plurality of termsin the new user query according to an expected scope of search resultsfor the new user query. In some embodiments, step 304 includes forming athird filter for the new user query, and forming a third filtered itemcollection by applying the third filter to the first filtered itemcollection. In some aspects, the third filtered item collection and thesecond filtered item collection do not overlap.

Step 306 includes re-ordering the terms in the new user query accordingto the expected scope of the search results. In some embodiments, step306 includes retaining filtered and scored results for each term step inthe original query: “new york”: “new” “york.” Step 306 may includechoosing the ordering of the terms filtered to increase the number ofpredicted results (e.g., altering the sequence “new york,” and “yorknew”).

Accordingly, step 306 provides the advantage of increasing the universeof possible predictions. In some embodiments, step 306 may includesorting the order of the terms in a query according to the expectedreturn in number of search items: terms with smaller return are placedfirst. In some embodiments step 306 includes re-ordering the termsweighting more heavily in favor of sequences with more likelypredictions. For example, the query “new york” is expected to returnmany more likely predictions relative to the query “york city,” and step306 may favor the foregoing ordering over the latter.

Step 307 includes forming a first filtered item collection. The searchengine stores IUQFR in the memory. In some embodiments, step 307includes applying the first filter to a plurality of image files storedin an image database.

Step 308 includes forming an additional filter for the new query (e.g.,using the first filtered item collection). In some embodiments, step 308includes performing a plurality of new predicted queries simultaneouslyor almost simultaneously because there is little to no overlappingbetween the search results for different predicted queries. Thissubstantially reduces the latency overhead of the intersected predictedsearch as disclosed herein.

Step 310 includes applying the additional filter to the filtered itemcollection to form an additional filtered item collection. In someembodiments, step 310 includes applying the second filter to the firstfiltered item collection.

Step 312 includes associating an item score to the items in the firstfiltered item collection and in the additional filtered item collection.Scoring items according to relevance. In some embodiments, step 312includes associating a probability to the one of the plurality of itemsthat a user clicks on a thumbnail associated with the one of theplurality of items based on an interaction history information and arelevance feedback information. In some embodiments, step 312 includesfinding a correlation between the first user query and an interactionhistory information associated with the user.

Step 314 includes sorting the items in the first filtered itemcollection and in the additional filtered item collection according tothe item scores. In some embodiments, step 314 includes sorting theresults by relevance to the query.

Step 316 includes providing a response to the first user query. In someembodiments step 316 includes forming a data structure. The results ofeach of the predicted query retrievals as well as the IUQFR are combinedinto a single result set. The data structure of the result set is a setof sets: One inner set for each predicted query and the IUQ. Each innerset contains the query and a sorted list of results, as with a standardsearch system. The resulting plurality of result sets can then be usedto provide the user with instantaneous result to his/her likely nextquery by displaying them in the results panel, as the user hovers apointing device over links, with updated results as suggestions insearch textbox, or any other display option.

FIG. 4 is a block diagram illustrating a process 400 used to obtain aset of predicted queries 445 from an IUQ 401 and an interaction historyinformation from interaction history database 254. A user enters aninput user query (IUQ) 401 into search engine 242. Search block 242filters query stream log 246 using IUQ 401 to obtain a plurality of ‘k’new queries 411-1 through 411-n, and through 411-k (where “n” is aninteger less than or equal to “k”). New queries 411-1 through 411-k willbe collectively referred to, hereinafter, as “new queries 411.” Newqueries 411 form a set IUQFR 425. New queries 411 are text stringsincluding IUQ in any position within the string and an additional textstring. New queries 411 are semantically meaningful expressions in thechosen language for search engine 242. For example, new query 411-1 is atext string starting with IUQ 401 and ending with a string “A₁,” and newquery 411-2 is a text string starting with a portion of string “A₂,”followed by IUQ 401, and ending with the remaining portion of string“A₂.”

Historical query information from interaction history 254 is used todetermine, for each of new queries 411-i (where “i” is an integerbetween 1 and “k”) including strings “A_(t),” a probability (“PA_(i)”)indicating the frequency of past usage of new query 411-i. Processor 236then sorts new queries 411 according to decreasing order of probability.Furthermore, processor 236 forms a set 445 of predicted queries 421-1through 421-n by selecting the n new queries 411 having the highestvalues of PAi (where i is less than or equal to n). Hereinafter,predicted queries 421-1 through 421-n will be collectively referred toas predicted queries 421. For each of the n predicted queries 421, thesystem determines the terms “A′_(i),” which may include portions beforeor after IUQ 401, or a combination. Note that predicted queries 421 (andterms Ai) are a re-ordered subset of new queries 411 (and terms “A′i”).

Processor 236 then forms a search thread 431-i for each of predictedqueries 421-i (where i is an integer between 1 and n). Search threads431-1 through 431-n will be collectively referred to hereinafter as“search threads 431.” Search engine 242 then performs a search of imagemetadata database 252 for each of search threads 431. Withoutlimitation, it is expected that the sets of images found by searchengine 242 for each of search threads 431 will be a subset of the imagesfound by search engine 242 applied to only IUQ 401. For example, theimage results for the query “woman in red dress” (e.g., a predictedquery 421-i) are expected to form a subset of the image results for thequery “woman in dress” (e.g., IUQ). A set of image results for eachsearch thread 431-i may be sorted according to a relevance of each ofthe images in the set of image results to the predicted query 421-i. Insome embodiments, search threads 431 can be performed simultaneously oralmost simultaneously because it is expected that the sets of imageresults may be non-overlapping subsets of the set of image results forIUQ 401.

In an exemplary embodiment, a user issues a query for “New York” (IUQ),which the web application passes to the search engine. The search enginewould find all of the items (i.e. images from image metadata database252) relevant for “New York” (IUQFR 425), store those in memory 232,then sort them by relevance to the query. In a separate thread orprocess, the search system uses historical behavioral data to predictthe queries containing “New York” that are likely to be issued next,such as “New York City”, “New York State”, “New York skyline”, “New Yorktaxi”, and the like. Any query not containing the terms “New” and “York”may not be returned by the prediction process. Because items relevant tothe predicted queries are also relevant to the query “New York,” thesearch system can use UIQFR 425 instead of re-filtering for “New” and“York,” separately. For each predicted query, the system will create anew search thread which will determine the additional terms (e.g., allterms that are not “New” and “York”), retrieve UIQFR 425, filter imagemetadata database 252 for the additional term(s), and sort the furtherfiltered requests. The results of each of these predicted queryretrievals as well as the results for the retrieval of the UIQ arejoined in a single set of result sets: one set for each of “New York,“New York City”, “New York State”, “New York skyline”, “New York taxi”,and the like. The set of result sets is returned to a results panel inthe user interface. The user interface displays these results to theuser in a predefined manner (multiple results, hover, textboxsuggestions, etc.)

In some embodiments a search system can filter by a multi-step process,where each step filters out a single term within the query. Thisstrategy can be used to expand the scope predicted queries by storing inIUQFR 425 the results of all the intermediate steps in filtering inaddition to the final filtered results for the original user query. Thismodification expands the scope of predicted queries to those thatcontain all the terms in any of the intermediate steps. Then, for eachpredicted query, further filtering is performed against the results ofthe step that filtered the most results and contains only terms that arein the predicted query. As an example, if the user searches for “NewYork City”, and the terms are filtered in the order “York”, “New”,“city”, the search engine will have the intermediate steps [[“York”],[“York”, “New”], [“York”, “New”, “city”]]. This allows the system toinclude predictions such as “New York skyline”. For “New York skyline”the filtering step will use the results of [“York”, “New”] as a startingpoint and further filter on “skyline.” Accordingly, the additionalmemory overhead and the added computation for determining for eachpredicted query what is the largest initial filter step is compensatedby a much larger scope of the image search results. Furthermore, aminimum threshold for the number of filtered documents may need to beapplied to maintain the efficiency provided by our disclosure.

FIG. 5 illustrates an exemplary user interface 500 of a search engineconsistent with the present disclosure. The user enters an IUQ into asearch field 501 (e.g., “chocolate”). User interface 500 displays apull-down menu 510 listing a plurality of predicted queries 521-1(“chocolate bar”), 521-2 (“chocolate cake”), 521-3 (“chocolate splash”),521-4 (“chocolate background”), 521-5 (“chocolates”), 521-6 (“chocolatebox”), and 521-7 (“chocolate milk”), and the like (collectively referredhereinafter as “predicted queries 521”).

A results panel 530 includes a plurality of thumbnails for result images531-1 through 531-13 (collectively referred to, hereinafter, as resultimages 531). The user may then direct a pointing device 515 to each ofresult images 531 to inspect, enlarge, edit, and/or download.

FIG. 6 is a walk-through illustration for searching an image based on apredicted user query from a first user query and a historical query log,according to some embodiments. The user enters an input user query 601and server 130 provides a plurality of intersected predicted queries 620that the user may select, or modify before activating the searchcommand. When a first search is complete, the server provides a resultspanel 630 including a plurality of result sets 631-1, 631-2, 631-3, and631-4 (hereinafter, collectively referred to as result sets 631). Eachof result sets 631 includes a plurality of thumbnails corresponding toimages retrieved by the search engine based on one of the intersectedpredicted queries.

In some embodiments, the images in each of result sets 621 aredifferent, which means that the search engine has extended the searchscope efficiently, thus providing a wider and more relevant range ofoptions to the user.

FIG. 7 is a flow chart illustrating steps in a method 700 to predict auser query based on a first user query and a historical query log.Method 700 may be performed at least partially by any one of networkservers hosting a collection of images, videos, and multimedia files(e.g., images and video clips), while communicating with any one of aplurality of client devices (e.g., any one of servers 130 and any one ofclients 110). The client devices may be handled by a user, wherein theuser may be registered to a private account with the server, or may be avisitor to the server website or logged in a server applicationinstalled in the client device. At least some of the steps in method 700may be performed by a computer having a processor executing commandsstored in a memory of the computer (e.g., processors 212 and 236,memories 220 and 232). Further, steps as disclosed in method 700 mayinclude retrieving, editing, and/or storing files in a database that ispart of, or is communicably coupled to, the computer, using, inter-alia,an image search engine (e.g., image search engine 242). The database mayinclude any one of an image metadata database and an interaction historydatabase (e.g., image database 252 and interaction history database254). Methods consistent with the present disclosure may include atleast some, but not all of the steps illustrated in method 700,performed in a different sequence. Furthermore, methods consistent withthe present disclosure may include at least two or more steps as inmethod 700 performed overlapping in time, or almost simultaneously.

Step 702 includes selecting at least one term from an input query. Step704 includes forming a filter result set of query strings from a querystream log and the at least one term in the input query.

Step 706 includes, for a query string in the filter result set,determining a probability that a non-matching portion in a query stringfrom the set of query strings is associated with the at least one termin the input query.

Step 708 includes sorting the filter result set according to ahistorical behavioral log. In some embodiments, step 708 includesdetermining a score for each item in the filter result set according toa frequency of appearance of the item in the historical behavioral log.Moreover, the historical behavioral log may include data for a specificuser, or aggregated data for multiple users.

Step 710 includes adding the query string to a set of predicted queries.In some embodiments, step 710 may include performing a permutation ofthe order of the terms in the query string to find the permutation thathas the potential for a larger results set for the search.

Step 712 includes verifying whether most or all the terms have beenincluded in the query string.

When all terms in the input query have been included in the querystring, in some embodiments step 714 includes forming a search threadfor at least one of the predicted queries in the set of predictedqueries.

FIG. 8 is a flow chart illustrating steps in a method to select apredicted user query provided to a client device through a userinterface. Method 800 may be performed at least partially by any one ofnetwork servers hosting a collection of images, videos, and multimediafiles (e.g., images and video clips), while communicating with any oneof a plurality of client devices (e.g., any one of servers 130 and anyone of clients 110). The client devices may be handled by a user,wherein the user may be registered to a private account with the server,or may be a visitor to the server website or logged in a serverapplication installed in the client device. At least some of the stepsin method 800 may be performed by a computer having a processorexecuting commands stored in a memory of the computer (e.g., processors212 and 236, memories 220 and 232). Further, steps as disclosed inmethod 800 may include retrieving, editing, and/or storing files in adatabase that is part of, or is communicably coupled to, the computer,using, inter-alia, an image search engine (e.g., image search engine242). The database may include any one of an image database, aninteraction history database, a training database, or an annotatedtraining database (e.g., image database 252 and interaction historydatabase 254). Methods consistent with the present disclosure mayinclude at least some, but not all of the steps illustrated in method800, performed in a different sequence. Furthermore, methods consistentwith the present disclosure may include at least two or more steps as inmethod 800 performed overlapping in time, or almost simultaneously.

Step 802 includes providing a query for an image search in a userinterface for a search engine. Step 804 includes selecting a predictedquery form a pull down menu in the user interface. In some embodiments,step 804 includes directing a pointing device to the predicted query inthe pull down menu and activating the selection with the pointingdevice.

Step 806 includes placing an additional term in the query, to focus thesearch scope. In some embodiments, step 806 includes appending a termthat is not included in the predicted query from the pull down menu tothe predicted query. Step 808 includes providing a search command to thesearch engine. Step 810 includes verifying that a results panel includesa satisfactory search scope.

When the results panel does not include a satisfactory search scope instep 810, step 812 includes re-ordering terms in a new query andrepeating steps 808 through 810. When the results panel includes asatisfactory search scope in step 810, step 814 includes selecting animage from a thumbnail in the results panel.

Hardware Overview

FIG. 9 is a block diagram illustrating an exemplary computer system 900with which the client 110 and server 130 of FIG. 1 can be implemented.In certain aspects, the computer system 900 may be implemented usinghardware or a combination of software and hardware, either in adedicated server, or integrated into another entity, or distributedacross multiple entities.

Computer system 900 (e.g., client 110 and server 130) includes a bus 908or other communication mechanism for communicating information, and aprocessor 902 (e.g., processors 212 and 236) coupled with bus 908 forprocessing information. By way of example, the computer system 900 maybe implemented with one or more processors 902. Processor 902 may be ageneral-purpose microprocessor, a microcontroller, a Digital SignalProcessor (DSP), an Application Specific Integrated Circuit (ASIC), aField Programmable Gate Array (FPGA), a Programmable Logic Device (PLD),a controller, a state machine, gated logic, discrete hardwarecomponents, or any other suitable entity that can perform calculationsor other manipulations of information.

Computer system 900 can include, in addition to hardware, code thatcreates an execution environment for the computer program in question,e.g., code that constitutes processor firmware, a protocol stack, adatabase management system, an operating system, or a combination of oneor more of them stored in an included memory 904 (e.g., memory 220 and232), such as a Random Access Memory (RAM), a flash memory, a Read OnlyMemory (ROM), a Programmable Read-Only Memory (PROM), an Erasable PROM(EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, orany other suitable storage device, coupled to bus 808 for storinginformation and instructions to be executed by processor 902. Theprocessor 902 and the memory 904 can be supplemented by, or incorporatedin, special purpose logic circuitry.

The instructions may be stored in the memory 904 and implemented in oneor more computer program products, i.e., one or more modules of computerprogram instructions encoded on a computer readable medium for executionby, or to control the operation of, the computer system 900, andaccording to any method well known to those of skill in the art,including, but not limited to, computer languages such as data-orientedlanguages (e.g., SQL, dBase), system languages (e.g., C, Objective-C,C++, Assembly), architectural languages (e.g., Java, .NET), andapplication languages (e.g., PHP, Ruby, Perl, Python). Instructions mayalso be implemented in computer languages such as array languages,aspect-oriented languages, assembly languages, authoring languages,command line interface languages, compiled languages, concurrentlanguages, curly-bracket languages, dataflow languages, data-structuredlanguages, declarative languages, esoteric languages, extensionlanguages, fourth-generation languages, functional languages,interactive mode languages, interpreted languages, iterative languages,list-based languages, little languages, logic-based languages, machinelanguages, macro languages, metaprogramming languages, multiparadigmlanguages, numerical analysis, non-English-based languages,object-oriented class-based languages, object-oriented prototype-basedlanguages, off-side rule languages, procedural languages, reflectivelanguages, rule-based languages, scripting languages, stack-basedlanguages, synchronous languages, syntax handling languages, visuallanguages, wirth languages, and xml-based languages. Memory 904 may alsobe used for storing temporary variable or other intermediate informationduring execution of instructions to be executed by processor 902.

A computer program as discussed herein does not necessarily correspondto a file in a file system. A program can be stored in a portion of afile that holds other programs or data (e.g., one or more scripts storedin a markup language document), in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, subprograms, or portions of code). A computerprogram can be deployed to be executed on one computer or on multiplecomputers that are located at one site or distributed across multiplesites and interconnected by a communication network. The processes andlogic flows described in this specification can be performed by one ormore programmable processors executing one or more computer programs toperform functions by operating on input data and generating output.

Computer system 900 further includes a data storage device 906 such as amagnetic disk or optical disk, coupled to bus 908 for storinginformation and instructions. Computer system 900 may be coupled viainput/output module 910 to various devices. Input/output module 910 canbe any input/output module. Exemplary input/output modules 910 includedata ports such as USB ports. The input/output module 910 is configuredto connect to a communications module 912. Exemplary communicationsmodules 912 (e.g., communications modules 218 and 238) includenetworking interface cards, such as Ethernet cards and modems. Incertain aspects, input/output module 910 is configured to connect to aplurality of devices, such as an input device 914 (e.g., input device214) and/or an output device 916 (e.g., output device 216). Exemplaryinput devices 914 include a keyboard and a pointing device, e.g., amouse or a trackball, by which a user can provide input to the computersystem 900. Other kinds of input devices 914 can be used to provide forinteraction with a user as well, such as a tactile input device, visualinput device, audio input device, or brain-computer interface device.For example, feedback provided to the user can be any form of sensoryfeedback, e.g., visual feedback, auditory feedback, or tactile feedback;and input from the user can be received in any form, including acoustic,speech, tactile, or brain wave input. Exemplary output devices 916include display devices, such as a LCD (liquid crystal display) monitor,for displaying information to the user.

According to one aspect of the present disclosure, the client 110 andserver 130 can be implemented using a computer system 900 in response toprocessor 902 executing one or more sequences of one or moreinstructions contained in memory 904. Such instructions may be read intomemory 904 from another machine-readable medium, such as data storagedevice 906. Execution of the sequences of instructions contained in mainmemory 904 causes processor 902 to perform the process steps describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the sequences of instructions contained inmemory 904. In alternative aspects, hard-wired circuitry may be used inplace of or in combination with software instructions to implementvarious aspects of the present disclosure. Thus, aspects of the presentdisclosure are not limited to any specific combination of hardwarecircuitry and software.

Various aspects of the subject matter described in this specificationcan be implemented in a computing system that includes a back endcomponent, e.g., as a data server, or that includes a middlewarecomponent, e.g., an application server, or that includes a front endcomponent, e.g., a client computer having a graphical user interface ora Web browser through which a user can interact with an implementationof the subject matter described in this specification, or anycombination of one or more such back end, middleware, or front endcomponents. The components of the system can be interconnected by anyform or medium of digital data communication, e.g., a communicationnetwork. The communication network (e.g., network 150) can include, forexample, any one or more of a LAN, a WAN, the Internet, and the like.Further, the communication network can include, but is not limited to,for example, any one or more of the following network topologies,including a bus network, a star network, a ring network, a mesh network,a star-bus network, tree or hierarchical network, or the like. Thecommunications modules can be, for example, modems or Ethernet cards.

Computer system 900 can include clients and servers. A client and serverare generally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other. Computer system 900can be, for example, and without limitation, a desktop computer, laptopcomputer, or tablet computer. Computer system 900 can also be embeddedin another device, for example, and without limitation, a mobiletelephone, a PDA, a mobile audio player, a Global Positioning System(GPS) receiver, a video game console, and/or a television set top box.

The term “machine-readable storage medium” or “computer readable medium”as used herein refers to any medium or media that participates inproviding instructions to processor 902 for execution. Such a medium maytake many forms, including, but not limited to, non-volatile media,volatile media, and transmission media. Non-volatile media include, forexample, optical or magnetic disks, such as data storage device 906.Volatile media include dynamic memory, such as memory 904. Transmissionmedia include coaxial cables, copper wire, and fiber optics, includingthe wires that comprise bus 908. Common forms of machine-readable mediainclude, for example, floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD, any other opticalmedium, punch cards, paper tape, any other physical medium with patternsof holes, a RAM, a PROM, an EPROM, a FLASH EPROM, any other memory chipor cartridge, or any other medium from which a computer can read. Themachine-readable storage medium can be a machine-readable storagedevice, a machine-readable storage substrate, a memory device, acomposition of matter effecting a machine-readable propagated signal, ora combination of one or more of them.

As used herein, the phrase “at least one of” preceding a series ofitems, with the terms “and” or “or” to separate any of the items,modifies the list as a whole, rather than each member of the list (i.e.,each item). The phrase “at least one of” does not require selection ofat least one item; rather, the phrase allows a meaning that includes atleast one of any one of the items, and/or at least one of anycombination of the items, and/or at least one of each of the items. Byway of example, the phrases “at least one of A, B, and C” or “at leastone of A, B, or C” each refer to only A, only B, or only C; anycombination of A, B, and C; and/or at least one of each of A, B, and C.

To the extent that the term “include,” “have,” or the like is used inthe description or the claims, such term is intended to be inclusive ina manner similar to the term “comprise” as “comprise” is interpretedwhen employed as a transitional word in a claim. The word “exemplary” isused herein to mean “serving as an example, instance, or illustration.”Any embodiment described herein as “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments.

A reference to an element in the singular is not intended to mean “oneand only one” unless specifically stated, but rather “one or more.” Allstructural and functional equivalents to the elements of the variousconfigurations described throughout this disclosure that are known orlater come to be known to those of ordinary skill in the art areexpressly incorporated herein by reference and intended to beencompassed by the subject technology. Moreover, nothing disclosedherein is intended to be dedicated to the public regardless of whethersuch disclosure is explicitly recited in the above description.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of what may be claimed, but ratheras descriptions of particular implementations of the subject matter.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

The subject matter of this specification has been described in terms ofparticular aspects, but other aspects can be implemented and are withinthe scope of the following claims. For example, while operations aredepicted in the drawings in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed, to achieve desirable results. The actionsrecited in the claims can be performed in a different order and stillachieve desirable results. As one example, the processes depicted in theaccompanying figures do not necessarily require the particular ordershown, or sequential order, to achieve desirable results. In certaincircumstances, multitasking and parallel processing may be advantageous.Moreover, the separation of various system components in the aspectsdescribed above should not be understood as requiring such separation inall aspects, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products. Othervariations are within the scope of the following claims.

What is claimed is:
 1. A computer-implemented method, comprising:receiving a first user query from a user for searching for an item in anitem database; forming a first filter based on the first user query;forming a first filtered item collection comprising a plurality of itemsfrom the item database; forming a new query with the first user queryand at least one term from a historical query log; re-ordering aplurality of terms in the new query according to an expected scope ofsearch results for a permutated new query; forming a second filter forthe new query; applying the second filter to the first filtered itemcollection to form a second filtered item collection; associating anitem score to each of a plurality of items in the first filtered itemcollection and in the second filtered item collection; sorting theplurality of items in the first filtered item collection and in thesecond filtered item collection according to the item score associatedto each of the plurality of items; and providing, for display to a userin a results panel, at least one item from the plurality of items in thefirst filtered item collection and in the second filtered itemcollection.
 2. The method of claim 1, wherein forming the first filtereditem collection comprises applying the first filter to a plurality ofimage files stored in an image database.
 3. The method of claim 1,wherein predicting a new query based on the first user query and ahistorical query log comprises matching at least a portion of the newquery with at least a portion of the first user query and a portion of aquery from the historical query log.
 4. The method of claim 1, whereinpredicting a new query based on the first user query and the historicalquery log comprises: forming a third filter for the new query; andforming a third filtered item collection by applying the third filter tothe first filtered item collection, wherein the third filtered itemcollection and the second filtered item collection do not overlap. 5.The method of claim 1, wherein forming the second filtered itemcollection comprises applying the second filter to the first filtereditem collection.
 6. The method of claim 1, wherein associating an itemscore to one of a plurality of items in the first filtered itemcollection and a plurality of items in the second filtered itemcollection comprises associating a probability with the one of theplurality of items for which a user clicks on a thumbnail associatedwith the one of the plurality of items, based on an interaction historyinformation and a relevance feedback information.
 7. The method of claim1, wherein associating an item score to each of a plurality of items inthe first filtered item collection and in the second filtered itemcollection comprises finding a correlation between the first user queryand interaction history information associated with the user.
 8. Themethod of claim 1, wherein associating an item score to each of aplurality of items in the first filtered item collection and in thesecond filtered item collection comprises finding a correlation betweenthe first user query and object recognition information associated witheach of the plurality of items and associating a higher item score for ahigher correlation between the first user query and object recognitioninformation.
 9. A system comprising: one or more processors to: receivea first user query from a user for searching for an item in an itemdatabase; form a first filter based on the first user query; form afirst filtered item collection comprising a plurality of items from theitem database; determine a probability that a non-matching portion isassociated with at least one term from the first user query for apredicted query string in a second filter; sort the plurality of itemsin the first filtered item collection and in a second filtered itemcollection according to an interaction history information; add thepredicted query string to a plurality of predicted queries; form asecond filter for a new query; apply the second filter to the firstfiltered item collection to form a second filtered item collection;associate an item score to each of a plurality of items in the firstfiltered item collection and in the second filtered item collection;sort the plurality of items in the first filtered item collection and inthe second filtered item collection according to the item scoreassociated to each of the plurality of items; and provide, for displayto a user in a results panel, at least one item from the plurality ofitems in the first filtered item collection and in the second filtereditem collection.
 10. The system of claim 9, wherein the one or moreprocessors further select at least one term from the first user query,and to form the first filter based on the first user query, based on apredicted query string from a query stream log and the at least one termfrom the first user query.
 11. The system of claim 9, wherein the one ormore processors form an item search thread for the new query.
 12. Thesystem of claim 9, wherein to predict a new query based on the firstuser query and a historical query log, the one or more processorsfurther re-order a plurality of terms in the new query according to anexpected scope of search results for the new query.
 13. The system ofclaim 9, wherein to predict a new query based on the first user queryand a historical query log the one or more processors match at least aportion of the new query with at least a portion of the first user queryand a portion of a query from the historical query log.
 14. The systemof claim 9, wherein the one or more processors further display apull-down menu for a user that includes the new query.
 15. The system ofclaim 9, wherein to predict a new query based on the first user queryand a historical query log the one or more processors: form the newquery with the first user query and at least one term from thehistorical query log; and re-order a plurality of terms in the new queryaccording to an expected scope of search results for a permutated newquery.
 16. The system of claim 9, wherein to predict a new query basedon the first user query and a historical query log the one or moreprocessors: form a third filter for the new query; and form a thirdfiltered item collection by applying the third filter to the firstfiltered item collection, wherein the third filtered item collection andthe second filtered item collection do not overlap.
 17. The system ofclaim 9, wherein to form the second filtered item collection the one ormore processors apply the second filter to the first filtered itemcollection.
 18. A non-transitory, computer-readable medium storinginstructions which, when executed by a processor, cause a computer toperform a method, the method comprising: receiving a first user queryfrom a user for searching for an item in an item database; forming afirst filter based on the first user query; forming a first filtereditem collection comprising a plurality of items from the item database;forming a new query with the first user query and at least one term froma historical query log; re-ordering a plurality of terms in the newquery according to an expected scope of search results for a permutated,new query; forming a second filter for the new query; applying thesecond filter to the first filtered item collection to form a secondfiltered item collection; associating an item score to each of aplurality of items in the first filtered item collection and in thesecond filtered item collection; sorting the plurality of items in thefirst filtered item collection and in the second filtered itemcollection according to the item score associated to each of theplurality of items; and providing, for display to a user in a resultspanel, at least one item from the plurality of items in the firstfiltered item collection and in the second filtered item collection. 19.The non-transitory, computer-readable medium of claim 18, wherein in themethod, forming the first filtered item collection comprises applyingthe first filter to a plurality of image files stored in an imagedatabase.
 20. The non-transitory, computer-readable medium of claim 18,wherein in the method, predicting a new query based on the first userquery and a historical query log comprises matching at least a portionof the new query with at least a portion of the first user query and aportion of a query from the historical query log.